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RDDpred: a condition-specific RNA-editing prediction model from RNA-seq data
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Min-su | - |
dc.contributor.author | Hur, Benjamin | - |
dc.contributor.author | Kim, Sun | - |
dc.date.accessioned | 2017-02-07T06:29:54Z | - |
dc.date.available | 2017-02-07T06:29:54Z | - |
dc.date.issued | 2016-01-11 | - |
dc.identifier.citation | BMC Genomics, 17(Suppl 1):5 | ko_KR |
dc.identifier.uri | https://hdl.handle.net/10371/100485 | - |
dc.description | This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any
medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. | ko_KR |
dc.description.abstract | Abstract
Background RNA-editing is an important post-transcriptional RNA sequence modification performed by two catalytic enzymes, "ADAR"(A-to-I) and "APOBEC"(C-to-U). By utilizing high-throughput sequencing technologies, the biological function of RNA-editing has been actively investigated. Currently, RNA-editing is considered to be a key regulator that controls various cellular functions, such as protein activity, alternative splicing pattern of mRNA, and substitution of miRNA targeting site. DARNED, a public RDD database, reported that there are more than 300-thousands RNA-editing sites detected in human genome(hg19). Moreover, multiple studies suggested that RNA-editing events occur in highly specific conditions. According to DARNED, 97.62 % of registered editing sites were detected in a single tissue or in a specific condition, which also supports that the RNA-editing events occur condition-specifically. Since RNA-seq can capture the whole landscape of transcriptome, RNA-seq is widely used for RDD prediction. However, significant amounts of false positives or artefacts can be generated when detecting RNA-editing from RNA-seq. Since it is difficult to perform experimental validation at the whole-transcriptome scale, there should be a powerful computational tool to distinguish true RNA-editing events from artefacts. Result We developed RDDpred, a Random Forest RDD classifier. RDDpred reports potentially true RNA-editing events from RNA-seq data. RDDpred was tested with two publicly available RNA-editing datasets and successfully reproduced RDDs reported in the two studies (90 %, 95 %) while rejecting false-discoveries (NPV: 75 %, 84 %). Conclusion RDDpred automatically compiles condition-specific training examples without experimental validations and then construct a RDD classifier. As far as we know, RDDpred is the very first machine-learning based automated pipeline for RDD prediction. We believe that RDDpred will be very useful and can contribute significantly to the study of condition-specific RNA-editing. RDDpred is available at http://biohealth.snu.ac.kr/software/RDDpred . | ko_KR |
dc.language.iso | en | ko_KR |
dc.publisher | BioMed Central | ko_KR |
dc.subject | RNA-editing | ko_KR |
dc.subject | Condition-specific | ko_KR |
dc.subject | Machine-learning | ko_KR |
dc.subject | Random forest | ko_KR |
dc.subject | RNA-seq | ko_KR |
dc.subject | Systematic artefact | ko_KR |
dc.title | RDDpred: a condition-specific RNA-editing prediction model from RNA-seq data | ko_KR |
dc.type | Article | ko_KR |
dc.contributor.AlternativeAuthor | 김민수 | - |
dc.contributor.AlternativeAuthor | 허벤자민 | - |
dc.contributor.AlternativeAuthor | 김선 | - |
dc.identifier.doi | 10.1186/s12864-015-2301-y | - |
dc.language.rfc3066 | en | - |
dc.rights.holder | Kim et al. | - |
dc.date.updated | 2017-01-06T10:07:51Z | - |
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